Alpha_waves_csp_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac7f70d5-ed1a-4bde-b69e-9118bfd8bed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy import signal\n",
    "from scipy import linalg \n",
    "\n",
    "import mne\n",
    "\n",
    "# Global variables\n",
    "sampling_rate = 250\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "85c4191c-aacb-415c-8f82-f8be80ebaa9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Extracting data for 8 channel data\"\"\"\n",
    "# Function to read files, put data in lists, returns an array with 8 entries, each one being a list of all values for a channel\n",
    "def extract_channel_vals_8_channels(data_file_path):\n",
    "    \n",
    "    data_file = open(data_file_path, 'r')\n",
    "    \n",
    "    channel_vals = [ [], [], [], [], [], [], [], [] ]\n",
    "    \n",
    "    for line in data_file:\n",
    "\n",
    "        temp = line.strip().split(\",\")\n",
    "        \n",
    "        try:\n",
    "            for i in range(len(channel_vals)):\n",
    "                \n",
    "                # Need to skip first column of text file since it has sample index info\n",
    "                channel_vals[i].append(float(temp[i + 1]))\n",
    "\n",
    "        except:\n",
    "            pass  \n",
    "    \n",
    "    data_file.close()\n",
    "        \n",
    "    # Convert lists to arrays\n",
    "    for channel_vals_i in [channel_vals[0], channel_vals[1], channel_vals[2], channel_vals[3],\n",
    "                         channel_vals[4], channel_vals[5], channel_vals[6], channel_vals[7], ]:\n",
    "        channel_vals_i = np.array(channel_vals_i)\n",
    "    \n",
    "\n",
    "    \n",
    "    channel_vals = np.array(channel_vals)\n",
    "    \n",
    "    return channel_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3c0d17b4-4670-4705-9c62-998498084317",
   "metadata": {},
   "outputs": [],
   "source": [
    "subject_1_data_1_path = r'C:\\Users\\alexm\\OneDrive\\Computer Files\\EEG\\Recordings\\OpenBCISession_2021-10-16_15-40-46_Subject1_Protocol1\\OpenBCI-RAW-2021-10-16_15-42-09.txt'\n",
    "subject_2_data_1_path = r'C:\\Users\\alexm\\OneDrive\\Computer Files\\EEG\\Recordings\\OpenBCISession_2021-10-16_15-08-43_Subject2_Protocol1\\OpenBCI-RAW-2021-10-16_15-10-34.txt'\n",
    "subject_4_data_1_path = r'C:\\Users\\alexm\\OneDrive\\Computer Files\\EEG\\Recordings\\OpenBCISession_2021-10-16_14-51-59_Subject4_Protocol1\\OpenBCI-RAW-2021-10-16_14-55-24.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "323c2e31-ee4f-47a2-a2a6-c748792545f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create EEG vector for a single 2s epoch\n",
    "\n",
    "\"\"\"\n",
    "Plan to get final input vector X for CSP:\n",
    "    1. Split channel values into 2s epochs (3s to 15s, 15s to 27s, 33s - 45s, 45-57s, all have 6 2-second epochs)\n",
    "    2. Filter channel values into alpha range\n",
    "    3. Create matrix x for each epoch (8 x 500 for 8 channels, 500 samples (2 * sampling rate))\n",
    "    4. Create vectors X (open) and X (closed) for training open and training closed data using specific epochs for those\n",
    "    5. X vectors are input vectors for CSP\n",
    "\"\"\"\n",
    "def get_epoch_vector(channel_vals, start_time):\n",
    "    \n",
    "    sampling_rate = 250\n",
    "    \n",
    "    low_filter = 5 #Hz\n",
    "    high_filter = 15 #Hz\n",
    "    b, a = signal.butter(5, [low_filter/250, high_filter/250], \"bandpass\")\n",
    "    \n",
    "    counter = start_time\n",
    "    \n",
    "    # Use bandpass filter to filter all channels into alpha range (5-15 Hz)\n",
    "    channel_vals_filtered = channel_vals[:]\n",
    "    for i in range (8):\n",
    "        channel_vals_filtered[i] = signal.filtfilt(b, a, channel_vals[i])\n",
    "\n",
    "    # Create (8 x 500) matrix x for a 2s epoch\n",
    "    \n",
    "    epoch_vector = [[], [], [], [], [], [], [], []]\n",
    "    for i in range(8):\n",
    "        for j in range(counter * sampling_rate, (counter + 2) * sampling_rate):\n",
    "            epoch_vector[i].append(channel_vals_filtered[i][j])\n",
    "\n",
    "    epoch_vector = np.array(epoch_vector)\n",
    "\n",
    "    #print(np.shape(epoch_vector))\n",
    "    \n",
    "    return epoch_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c0ddd3bc-6e73-4ff7-a21e-d5955ab5ae10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.3864041 ,   0.2295811 ,   0.0201083 , ...,  -1.16368571,\n",
       "         -1.13146769,  -1.04755202],\n",
       "       [  1.68812418,   1.85687377,   1.94361284, ...,   1.05981832,\n",
       "          1.17026009,   1.32054448],\n",
       "       [  7.38416936,   7.03543841,   6.50276352, ...,   0.17002761,\n",
       "         -0.08521103,  -0.31383634],\n",
       "       ...,\n",
       "       [  0.02979931,   0.61357367,   1.24680734, ..., -14.01227113,\n",
       "        -11.31223022,  -8.64491534],\n",
       "       [ 10.90559444,  10.33550158,   9.68655769, ...,   4.42235084,\n",
       "          5.12657918,   5.70372474],\n",
       "       [  5.45667503,   5.31630237,   5.06418622, ...,  -2.03590526,\n",
       "         -2.60942343,  -3.13268406]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_epoch_vector(extract_channel_vals_8_channels(subject_2_data_1_path), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d8d7df11-5b07-4381-b9e6-f6bb9ae1d01c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create input vector for CSP, X consisting of start time and next 12 seconds, split into 6 2-second epochs\n",
    "\n",
    "def get_input_vector(channel_vals, start_time):\n",
    "    \n",
    "    csp_vector = []\n",
    "    \n",
    "    for epoch in range(start_time, start_time + 12, 2):\n",
    "        csp_vector.append(get_epoch_vector(channel_vals, epoch))\n",
    "        \n",
    "    for epoch in range(start_time + 30, start_time + 42, 2):\n",
    "        csp_vector.append(get_epoch_vector(channel_vals, epoch))\n",
    "    \n",
    "    csp_vector = np.array(csp_vector)\n",
    "    \n",
    "    #print(np.shape(csp_vector))\n",
    "    \n",
    "    return csp_vector\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "af9b305f-d19f-4f83-b19c-f49fb3ceb29d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get_input_vector(extract_channel_vals_8_channels(subject_2_data_1_path), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6f89e2fc-c3a6-413c-ab07-0d2381540e0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return all 4 input vectors for CSP - eyes open training, eyes closed training, eyes open testing, eyes open testing\n",
    "\n",
    "def get_all_input_vectors(channel_vals):\n",
    "    \n",
    "    eyes_open_training = get_input_vector(extract_channel_vals_8_channels(subject_2_data_1_path), 3)\n",
    "    eyes_closed_training = get_input_vector(extract_channel_vals_8_channels(subject_2_data_1_path), 33)\n",
    "    eyes_open_testing = get_input_vector(extract_channel_vals_8_channels(subject_2_data_1_path), 15)\n",
    "    eyes_closed_testing = get_input_vector(extract_channel_vals_8_channels(subject_2_data_1_path), 45)\n",
    "    \n",
    "    return eyes_open_training, eyes_closed_training, eyes_open_testing, eyes_closed_testing\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4c596ad9-e157-48c0-a8eb-cb3bf942d527",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 8, 500) (12, 8, 500)\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.2 (2.2e-16 eps * 8 dim * 7e+14  max singular value)\n",
      "    Estimated rank (mag): 8\n",
      "    MAG: rank 8 computed from 8 data channels with 0 projectors\n",
      "Reducing data rank from 8 -> 8\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.5 (2.2e-16 eps * 8 dim * 8.6e+14  max singular value)\n",
      "    Estimated rank (mag): 8\n",
      "    MAG: rank 8 computed from 8 data channels with 0 projectors\n",
      "Reducing data rank from 8 -> 8\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.1 (2.2e-16 eps * 8 dim * 6.2e+14  max singular value)\n",
      "    Estimated rank (mag): 8\n",
      "    MAG: rank 8 computed from 8 data channels with 0 projectors\n",
      "Reducing data rank from 8 -> 8\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1 (2.2e-16 eps * 8 dim * 5.9e+14  max singular value)\n",
      "    Estimated rank (mag): 8\n",
      "    MAG: rank 8 computed from 8 data channels with 0 projectors\n",
      "Reducing data rank from 8 -> 8\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "[[-1.85806348 -0.01658714 -0.00360987 -1.05976644]\n",
      " [-1.99397326 -0.13785538 -0.08030472 -0.40900973]\n",
      " [-1.63185668 -0.46807992 -0.98336458 -1.47995602]\n",
      " [-1.83750183  0.12480359 -0.81261566 -1.30350734]\n",
      " [-1.48317722 -0.89112122 -0.30071069 -1.20632148]\n",
      " [-1.77213264 -1.28517122 -0.52959864 -1.78934883]\n",
      " [ 1.06108337 -1.27849216 -1.62188881  0.1878874 ]\n",
      " [-0.10125703 -1.98640752 -1.57122782 -0.328128  ]\n",
      " [-1.36254809 -1.22688496 -0.99777147 -0.49371679]\n",
      " [-0.90676764 -1.09738801 -0.79295384 -0.41085982]\n",
      " [-1.65411426 -1.21068714 -1.04089905 -0.99122076]\n",
      " [-1.171477   -1.03081926 -1.04347073 -0.79841941]]\n"
     ]
    }
   ],
   "source": [
    "training = get_input_vector(extract_channel_vals_8_channels(subject_2_data_1_path), 3)\n",
    "testing = get_input_vector(extract_channel_vals_8_channels(subject_2_data_1_path), 15)\n",
    "print(np.shape(training), np.shape(testing))\n",
    "labels = np.array([1,1,1,1,1,1,2,2,2,2,2,2])\n",
    "csp = mne.decoding.CSP(n_components=4, norm_trace=False)\n",
    "training_x = csp.fit_transform(training, labels)\n",
    "testing_x = csp.fit_transform(testing, labels)\n",
    "print(training_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7f0f66ee-79fc-41bb-978c-282a21d3c40d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "train_y = [0,0,0,0,0,0,1,1,1,1,1,1]\n",
    "\n",
    "clf_svm = svm.SVC(kernel = 'linear')\n",
    "clf_svm.fit(training_x, train_y)\n",
    "\n",
    "print(clf_svm.score(testing_x, train_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b645340e-d6c8-4376-924a-806a6649ba47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Measurement date</th>\n",
       "<td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Experimenter</th>\n",
       "<td>Unknown</td>\n",
       "    </tr>\n",
       "        <th>Participant</th>\n",
       "<td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Digitized points</th>\n",
       "        <td>11 points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Good channels</th>\n",
       "        <td>8 EEG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Bad channels</th>\n",
       "        <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>EOG channels</th>\n",
       "        <td>Not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>ECG channels</th>\n",
       "        <td>Not available</td>\n",
       "    <tr>\n",
       "        <th>Sampling frequency</th>\n",
       "        <td>250.00 Hz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Highpass</th>\n",
       "        <td>0.00 Hz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Lowpass</th>\n",
       "        <td>125.00 Hz</td>\n",
       "    </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<Info | 8 non-empty values\n",
       " bads: []\n",
       " ch_names: Fp1, Fp2, C3, C4, O1, O2, P4, P3\n",
       " chs: 8 EEG\n",
       " custom_ref_applied: False\n",
       " dig: 11 items (3 Cardinal, 8 EEG)\n",
       " highpass: 0.0 Hz\n",
       " lowpass: 125.0 Hz\n",
       " meas_date: unspecified\n",
       " nchan: 8\n",
       " projs: []\n",
       " sfreq: 250.0 Hz\n",
       ">"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_names = [\"Fp1\", \"Fp2\", \"C3\", \"C4\", \"O1\", \"O2\", \"P4\", \"P3\"]\n",
    "info = mne.create_info(channel_names, sampling_rate, \"eeg\")\n",
    "info.set_montage(\"standard_alphabetic\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "53905f08-f366-4b15-ae01-1485290c1eac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.2 (2.2e-16 eps * 8 dim * 7e+14  max singular value)\n",
      "    Estimated rank (mag): 8\n",
      "    MAG: rank 8 computed from 8 data channels with 0 projectors\n",
      "Reducing data rank from 8 -> 8\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.5 (2.2e-16 eps * 8 dim * 8.6e+14  max singular value)\n",
      "    Estimated rank (mag): 8\n",
      "    MAG: rank 8 computed from 8 data channels with 0 projectors\n",
      "Reducing data rank from 8 -> 8\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<MNEFigure size 540x158.4 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<MNEFigure size 540x158.4 with 5 Axes>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csp.fit(training, labels)\n",
    "csp.plot_patterns(info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3599d66d-9e8e-40dc-bbce-0c10ecf21ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Look at energy spectrum of fit_transform outputs (CSP2 should be distinct from other ones)\n",
    "# Try CSP on data that is only filtered 0-60 hz, not into alpha band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "29a50abf-577b-45d4-8758-55d19eb48e4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.2 (2.2e-16 eps * 8 dim * 7e+14  max singular value)\n",
      "    Estimated rank (mag): 8\n",
      "    MAG: rank 8 computed from 8 data channels with 0 projectors\n",
      "Reducing data rank from 8 -> 8\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n",
      "Computing rank from data with rank=None\n",
      "    Using tolerance 1.5 (2.2e-16 eps * 8 dim * 8.6e+14  max singular value)\n",
      "    Estimated rank (mag): 8\n",
      "    MAG: rank 8 computed from 8 data channels with 0 projectors\n",
      "Reducing data rank from 8 -> 8\n",
      "Estimating covariance using EMPIRICAL\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "[csp_1, csp_2, csp_3, csp_4, csp_5, csp_6, csp_7, csp_8, csp_9, csp_10, csp_11, csp_12] = csp.fit_transform(training, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b9649d9b-bc68-42d7-81c9-82b903e25701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.85806348 -0.01658714 -0.00360987 -1.05976644]\n"
     ]
    }
   ],
   "source": [
    "print (csp_1)"
   ]
  }
 ],
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